Website providers often desire to collect data that describes usage and visitation patterns for their websites. For example, website providers may collect information about how a visitor navigates through their website. This data is often referred to as web analytics data. Such information can be extremely valuable in developing usage statistics for various purposes, including for example estimating server load, determining advertising rates, identifying areas of websites that are in need of redesign, providing targeted advertising, and the like.
Web analytics data is often collected via logfile analysis or page tagging. Logfile analysis includes reading logfiles that store a web server's transactions. Page tagging uses executable code (e.g., a “web-bug”) embedded in the webpage to transmit information about the user and their website visit when the webpage is executed by the visitor's browser application. The web analytics data is often gathered and stored at a web analytics provider to generate a database of web analytics data. The web analytics data may be processed to generate various web-analytics reports that can be used by a website administrator to assess and optimize their website. For example, a website provider may submit a query to a web analytics provider for a metric identifying what percentage of visitors are making purchases on the website, and the web analytics provider may process the stored data to provide the corresponding metric.
Over time, the amount of web analytics data collect can grow in size. For example, as the activity of the website and users increases, the amount of web-analytics data can increase dramatically. Thus, the web analytics data may have to be stored in a large database where it can be accessed for processing. Moreover, as the amount of analytics data grows larger, an increased amount of processing may be required to extract desired information from the analytics data. As a result, a web-analytics system may become complex, requiring a large amount of storage space to store the large amounts of web-analytics data and may require an increasing amount of processing to extract desired information from the web-analytics data. Further, some data may lose relevance over a period of time, decreasing its value to the web-analytics reports while still contributing to the complexities of storage and processing.
Accordingly, it is desirable to provide technique for efficiently managing (e.g., receiving, storing and processing) analytics data, such as web analytics data.